{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "'''\n",
    "【课程1.2】  分布分析\n",
    "\n",
    "分布分析 → 研究数据的分布特征和分布类型，分定量数据、定性数据区分基本统计量\n",
    "\n",
    "极差 / 频率分布情况 / 分组组距及组数\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "房屋编码      int64\n",
      "小区       object\n",
      "朝向       object\n",
      "房屋单价      int64\n",
      "参考首付    float64\n",
      "参考总价    float64\n",
      "经度      float64\n",
      "纬度      float64\n",
      "dtype: object\n",
      "-------\n",
      "数据长度为75条\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>房屋编码</th>\n",
       "      <th>小区</th>\n",
       "      <th>朝向</th>\n",
       "      <th>房屋单价</th>\n",
       "      <th>参考首付</th>\n",
       "      <th>参考总价</th>\n",
       "      <th>经度</th>\n",
       "      <th>纬度</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>605093949</td>\n",
       "      <td>大望新平村</td>\n",
       "      <td>南北</td>\n",
       "      <td>5434</td>\n",
       "      <td>15.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>114.180964</td>\n",
       "      <td>22.603698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>605768856</td>\n",
       "      <td>通宝楼</td>\n",
       "      <td>南北</td>\n",
       "      <td>3472</td>\n",
       "      <td>7.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>114.179298</td>\n",
       "      <td>22.566910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>606815561</td>\n",
       "      <td>罗湖区罗芳村</td>\n",
       "      <td>南北</td>\n",
       "      <td>5842</td>\n",
       "      <td>15.6</td>\n",
       "      <td>52.0</td>\n",
       "      <td>114.158869</td>\n",
       "      <td>22.547223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>605147285</td>\n",
       "      <td>兴华苑</td>\n",
       "      <td>南北</td>\n",
       "      <td>3829</td>\n",
       "      <td>10.8</td>\n",
       "      <td>36.0</td>\n",
       "      <td>114.158040</td>\n",
       "      <td>22.554343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>606030866</td>\n",
       "      <td>京基东方都会</td>\n",
       "      <td>西南</td>\n",
       "      <td>47222</td>\n",
       "      <td>51.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>114.149243</td>\n",
       "      <td>22.554370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        房屋编码      小区  朝向   房屋单价  参考首付   参考总价          经度         纬度\n",
       "0  605093949   大望新平村  南北   5434  15.0   50.0  114.180964  22.603698\n",
       "1  605768856     通宝楼  南北   3472   7.5   25.0  114.179298  22.566910\n",
       "2  606815561  罗湖区罗芳村  南北   5842  15.6   52.0  114.158869  22.547223\n",
       "3  605147285     兴华苑  南北   3829  10.8   36.0  114.158040  22.554343\n",
       "4  606030866  京基东方都会  西南  47222  51.0  170.0  114.149243  22.554370"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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7S0KENCk4hBCij9aa8vJyDh48yLH9B3A7nShlIC0ni8XLl5OXlzdkVVEhxNjIGI4QVFxc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\nlFK204JDKaWUUrb7/4ereqGfIxJ2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x98034a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 数据读取\n",
    "\n",
    "data = pd.read_csv('C:/Users/Hjx/Desktop/深圳罗湖二手房信息.csv',engine = 'python')\n",
    "plt.scatter(data['经度'],data['纬度'],  # 按照经纬度显示\n",
    "            s = data['房屋单价']/500,  # 按照单价显示大小\n",
    "            c = data['参考总价'],  # 按照总价显示颜色\n",
    "            alpha = 0.4, cmap = 'Reds')  \n",
    "plt.grid()\n",
    "print(data.dtypes)\n",
    "print('-------\\n数据长度为%i条' % len(data))\n",
    "data.head()\n",
    "# 通过数据可见，一共8个字段\n",
    "# 定量字段：房屋单价，参考首付，参考总价，*经度，*纬度，*房屋编码\n",
    "# 定性字段：小区，朝向"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "参考首付极差为 52.500000 \n",
      "参考总价极差为 175.000000\n"
     ]
    }
   ],
   "source": [
    "# 极差：max-min\n",
    "# 只针对定量字段\n",
    "\n",
    "def d_range(df,*cols):\n",
    "    krange = []\n",
    "    for col in cols:\n",
    "        crange = df[col].max() - df[col].min()\n",
    "        krange.append(crange)\n",
    "    return(krange)\n",
    "# 创建函数求极差\n",
    "\n",
    "key1 = '参考首付'\n",
    "key2 = '参考总价'\n",
    "dr = d_range(data,key1,key2)\n",
    "print('%s极差为 %f \\n%s极差为 %f' % (key1, dr[0], key2, dr[1]))\n",
    "# 求出数据对应列的极差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x983e320>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xb0979e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 频率分布情况 - 定量字段\n",
    "# ① 通过直方图直接判断分组组数\n",
    "\n",
    "data[key2].hist(bins=10)\n",
    "# 简单查看数据分布，确定分布组数 → 一般8-16即可\n",
    "# 这里以10组为参考"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      [42.5, 60)\n",
      "1      [25, 42.5)\n",
      "2      [42.5, 60)\n",
      "3      [25, 42.5)\n",
      "4    [165, 182.5)\n",
      "Name: 参考总价, dtype: category\n",
      "Categories (10, object): [[25, 42.5) < [42.5, 60) < [60, 77.5) < [77.5, 95) ... [130, 147.5) < [147.5, 165) < [165, 182.5) < [182.5, 200.175)] \n",
      "------\n",
      "[25, 42.5)          14\n",
      "[42.5, 60)          17\n",
      "[60, 77.5)           1\n",
      "[77.5, 95)           2\n",
      "[95, 112.5)          4\n",
      "[112.5, 130)         2\n",
      "[130, 147.5)         3\n",
      "[147.5, 165)         4\n",
      "[165, 182.5)         8\n",
      "[182.5, 200.175)    20\n",
      "Name: 参考总价, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>房屋编码</th>\n",
       "      <th>小区</th>\n",
       "      <th>朝向</th>\n",
       "      <th>房屋单价</th>\n",
       "      <th>参考首付</th>\n",
       "      <th>参考总价</th>\n",
       "      <th>经度</th>\n",
       "      <th>纬度</th>\n",
       "      <th>参考总价分组区间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>605093949</td>\n",
       "      <td>大望新平村</td>\n",
       "      <td>南北</td>\n",
       "      <td>5434</td>\n",
       "      <td>15.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>114.180964</td>\n",
       "      <td>22.603698</td>\n",
       "      <td>[42.5, 60)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>605768856</td>\n",
       "      <td>通宝楼</td>\n",
       "      <td>南北</td>\n",
       "      <td>3472</td>\n",
       "      <td>7.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>114.179298</td>\n",
       "      <td>22.566910</td>\n",
       "      <td>[25, 42.5)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>606815561</td>\n",
       "      <td>罗湖区罗芳村</td>\n",
       "      <td>南北</td>\n",
       "      <td>5842</td>\n",
       "      <td>15.6</td>\n",
       "      <td>52.0</td>\n",
       "      <td>114.158869</td>\n",
       "      <td>22.547223</td>\n",
       "      <td>[42.5, 60)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>605147285</td>\n",
       "      <td>兴华苑</td>\n",
       "      <td>南北</td>\n",
       "      <td>3829</td>\n",
       "      <td>10.8</td>\n",
       "      <td>36.0</td>\n",
       "      <td>114.158040</td>\n",
       "      <td>22.554343</td>\n",
       "      <td>[25, 42.5)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>606030866</td>\n",
       "      <td>京基东方都会</td>\n",
       "      <td>西南</td>\n",
       "      <td>47222</td>\n",
       "      <td>51.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>114.149243</td>\n",
       "      <td>22.554370</td>\n",
       "      <td>[165, 182.5)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        房屋编码      小区  朝向   房屋单价  参考首付   参考总价          经度         纬度  \\\n",
       "0  605093949   大望新平村  南北   5434  15.0   50.0  114.180964  22.603698   \n",
       "1  605768856     通宝楼  南北   3472   7.5   25.0  114.179298  22.566910   \n",
       "2  606815561  罗湖区罗芳村  南北   5842  15.6   52.0  114.158869  22.547223   \n",
       "3  605147285     兴华苑  南北   3829  10.8   36.0  114.158040  22.554343   \n",
       "4  606030866  京基东方都会  西南  47222  51.0  170.0  114.149243  22.554370   \n",
       "\n",
       "       参考总价分组区间  \n",
       "0    [42.5, 60)  \n",
       "1    [25, 42.5)  \n",
       "2    [42.5, 60)  \n",
       "3    [25, 42.5)  \n",
       "4  [165, 182.5)  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 频率分布情况 - 定量字段\n",
    "# ② 求出分组区间\n",
    "\n",
    "gcut = pd.cut(data[key2],10,right=False)\n",
    "gcut_count = gcut.value_counts(sort=False)  # 不排序\n",
    "data['%s分组区间' % key2] = gcut.values\n",
    "print(gcut.head(),'\\n------')\n",
    "print(gcut_count)\n",
    "data.head()\n",
    "# pd.cut(x, bins, right)：按照组数对x分组，且返回一个和x同样长度的分组dataframe，right → 是否右边包含，默认True\n",
    "# 通过groupby查看不同组的数据频率分布\n",
    "# 给源数据data添加“分组区间”列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "            <tr>\n",
       "                \n",
       "                <th class=\"blank\">\n",
       "                \n",
       "                <th class=\"col_heading level0 col0\">频数\n",
       "                \n",
       "                <th class=\"col_heading level0 col1\">频率\n",
       "                \n",
       "                <th class=\"col_heading level0 col2\">累计频率\n",
       "                \n",
       "                <th class=\"col_heading level0 col3\">频率%\n",
       "                \n",
       "                <th class=\"col_heading level0 col4\">累计频率%\n",
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       "                \n",
       "                <th id=\"T_67530202_316f_11e8_9ce0_240a64de810d\" class=\"row_heading level4 row0\">\n",
       "                    [25, 42.5)\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow0_col0\" class=\"data row0 col0\">\n",
       "                    14\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow0_col1\" class=\"data row0 col1\">\n",
       "                    0.186667\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow0_col2\" class=\"data row0 col2\">\n",
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       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow0_col3\" class=\"data row0 col3\">\n",
       "                    18.67%\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow0_col4\" class=\"data row0 col4\">\n",
       "                    18.67%\n",
       "                \n",
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       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_67530202_316f_11e8_9ce0_240a64de810d\" class=\"row_heading level4 row1\">\n",
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       "                \n",
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       "                    17\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow1_col1\" class=\"data row1 col1\">\n",
       "                    0.226667\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow1_col2\" class=\"data row1 col2\">\n",
       "                    0.413333\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow1_col3\" class=\"data row1 col3\">\n",
       "                    22.67%\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow1_col4\" class=\"data row1 col4\">\n",
       "                    41.33%\n",
       "                \n",
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       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_67530202_316f_11e8_9ce0_240a64de810d\" class=\"row_heading level4 row2\">\n",
       "                    [60, 77.5)\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow2_col0\" class=\"data row2 col0\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow2_col1\" class=\"data row2 col1\">\n",
       "                    0.0133333\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow2_col2\" class=\"data row2 col2\">\n",
       "                    0.426667\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow2_col3\" class=\"data row2 col3\">\n",
       "                    1.33%\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow2_col4\" class=\"data row2 col4\">\n",
       "                    42.67%\n",
       "                \n",
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       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_67530202_316f_11e8_9ce0_240a64de810d\" class=\"row_heading level4 row3\">\n",
       "                    [77.5, 95)\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow3_col0\" class=\"data row3 col0\">\n",
       "                    2\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow3_col1\" class=\"data row3 col1\">\n",
       "                    0.0266667\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow3_col2\" class=\"data row3 col2\">\n",
       "                    0.453333\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow3_col3\" class=\"data row3 col3\">\n",
       "                    2.67%\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow3_col4\" class=\"data row3 col4\">\n",
       "                    45.33%\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_67530202_316f_11e8_9ce0_240a64de810d\" class=\"row_heading level4 row4\">\n",
       "                    [95, 112.5)\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow4_col0\" class=\"data row4 col0\">\n",
       "                    4\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow4_col1\" class=\"data row4 col1\">\n",
       "                    0.0533333\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow4_col2\" class=\"data row4 col2\">\n",
       "                    0.506667\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow4_col3\" class=\"data row4 col3\">\n",
       "                    5.33%\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow4_col4\" class=\"data row4 col4\">\n",
       "                    50.67%\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_67530202_316f_11e8_9ce0_240a64de810d\" class=\"row_heading level4 row5\">\n",
       "                    [112.5, 130)\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow5_col0\" class=\"data row5 col0\">\n",
       "                    2\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow5_col1\" class=\"data row5 col1\">\n",
       "                    0.0266667\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow5_col2\" class=\"data row5 col2\">\n",
       "                    0.533333\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow5_col3\" class=\"data row5 col3\">\n",
       "                    2.67%\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow5_col4\" class=\"data row5 col4\">\n",
       "                    53.33%\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_67530202_316f_11e8_9ce0_240a64de810d\" class=\"row_heading level4 row6\">\n",
       "                    [130, 147.5)\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow6_col0\" class=\"data row6 col0\">\n",
       "                    3\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow6_col1\" class=\"data row6 col1\">\n",
       "                    0.04\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow6_col2\" class=\"data row6 col2\">\n",
       "                    0.573333\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow6_col3\" class=\"data row6 col3\">\n",
       "                    4.00%\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow6_col4\" class=\"data row6 col4\">\n",
       "                    57.33%\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_67530202_316f_11e8_9ce0_240a64de810d\" class=\"row_heading level4 row7\">\n",
       "                    [147.5, 165)\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow7_col0\" class=\"data row7 col0\">\n",
       "                    4\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow7_col1\" class=\"data row7 col1\">\n",
       "                    0.0533333\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow7_col2\" class=\"data row7 col2\">\n",
       "                    0.626667\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow7_col3\" class=\"data row7 col3\">\n",
       "                    5.33%\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow7_col4\" class=\"data row7 col4\">\n",
       "                    62.67%\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_67530202_316f_11e8_9ce0_240a64de810d\" class=\"row_heading level4 row8\">\n",
       "                    [165, 182.5)\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow8_col0\" class=\"data row8 col0\">\n",
       "                    8\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow8_col1\" class=\"data row8 col1\">\n",
       "                    0.106667\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow8_col2\" class=\"data row8 col2\">\n",
       "                    0.733333\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow8_col3\" class=\"data row8 col3\">\n",
       "                    10.67%\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow8_col4\" class=\"data row8 col4\">\n",
       "                    73.33%\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_67530202_316f_11e8_9ce0_240a64de810d\" class=\"row_heading level4 row9\">\n",
       "                    [182.5, 200.175)\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow9_col0\" class=\"data row9 col0\">\n",
       "                    20\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow9_col1\" class=\"data row9 col1\">\n",
       "                    0.266667\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow9_col2\" class=\"data row9 col2\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow9_col3\" class=\"data row9 col3\">\n",
       "                    26.67%\n",
       "                \n",
       "                <td id=\"T_67530202_316f_11e8_9ce0_240a64de810drow9_col4\" class=\"data row9 col4\">\n",
       "                    100.00%\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "        </tbody>\n",
       "        </table>\n",
       "        "
      ],
      "text/plain": [
       "<pandas.formats.style.Styler at 0xb116dd8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 频率分布情况 - 定量字段\n",
    "# ③  求出目标字段下频率分布的其他统计量 → 频数，频率，累计频率\n",
    "\n",
    "r_zj = pd.DataFrame(gcut_count)\n",
    "r_zj.rename(columns ={gcut_count.name:'频数'}, inplace = True)  # 修改频数字段名\n",
    "r_zj['频率'] = r_zj / r_zj['频数'].sum()  # 计算频率\n",
    "r_zj['累计频率'] = r_zj['频率'].cumsum()  # 计算累计频率\n",
    "r_zj['频率%'] = r_zj['频率'].apply(lambda x: \"%.2f%%\" % (x*100))  # 以百分比显示频率\n",
    "r_zj['累计频率%'] = r_zj['累计频率'].apply(lambda x: \"%.2f%%\" % (x*100))  # 以百分比显示累计频率\n",
    "r_zj.style.bar(subset=['频率','累计频率'], color='green',width=100)\n",
    "# 可视化显示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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r1vYh+o/PJe6+Ia3PsUQT1Xya6Bq777j7HW2pSUREOrSfxo9dRP9NuMTMKolOJX6I6HTx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\n/khsf/ZQDmvWUb+t/DlfAgwDOoBdgvQrgZeAnbB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      "text/plain": [
       "<matplotlib.figure.Figure at 0xb131f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 频率分布情况 - 定量字段\n",
    "# ④ 绘制频率直方图\n",
    "\n",
    "r_zj['频率'].plot(kind = 'bar',\n",
    "                 width = 0.8,\n",
    "                 figsize = (12,2),\n",
    "                 rot = 0,\n",
    "                 color = 'k',\n",
    "                 grid = True,\n",
    "                 alpha = 0.5)\n",
    "plt.title('参考总价分布频率直方图')\n",
    "# 绘制直方图\n",
    "\n",
    "x = len(r_zj)\n",
    "y = r_zj['频率']\n",
    "m = r_zj['频数']\n",
    "for i,j,k in zip(range(x),y,m):\n",
    "    plt.text(i-0.1,j+0.01,'%i' % k, color = 'k')\n",
    "# 添加频数标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "南北    29\n",
      "南     20\n",
      "东      8\n",
      "东南     5\n",
      "北      4\n",
      "西南     4\n",
      "西北     3\n",
      "东北     1\n",
      "东西     1\n",
      "Name: 朝向, dtype: int64\n"
     ]
    },
    {
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       "            #T_69b3902c_316f_11e8_8cc5_240a64de810drow7_col2 {\n",
       "            \n",
       "                width:  10em;\n",
       "            \n",
       "                 height:  80%;\n",
       "            \n",
       "                background:  linear-gradient(90deg,#d65f5f 97.82608695652176%, transparent 0%);\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_69b3902c_316f_11e8_8cc5_240a64de810drow8_col1 {\n",
       "            \n",
       "                width:  10em;\n",
       "            \n",
       "                 height:  80%;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_69b3902c_316f_11e8_8cc5_240a64de810drow8_col2 {\n",
       "            \n",
       "                width:  10em;\n",
       "            \n",
       "                 height:  80%;\n",
       "            \n",
       "                background:  linear-gradient(90deg,#d65f5f 100.0%, transparent 0%);\n",
       "            \n",
       "            }\n",
       "        \n",
       "        </style>\n",
       "\n",
       "        <table id=\"T_69b3902c_316f_11e8_8cc5_240a64de810d\" None>\n",
       "        \n",
       "\n",
       "        <thead>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th class=\"blank\">\n",
       "                \n",
       "                <th class=\"col_heading level0 col0\">频数\n",
       "                \n",
       "                <th class=\"col_heading level0 col1\">频率\n",
       "                \n",
       "                <th class=\"col_heading level0 col2\">累计频率\n",
       "                \n",
       "                <th class=\"col_heading level0 col3\">频率%\n",
       "                \n",
       "                <th class=\"col_heading level0 col4\">累计频率%\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "        </thead>\n",
       "        <tbody>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_69b3902c_316f_11e8_8cc5_240a64de810d\" class=\"row_heading level4 row0\">\n",
       "                    南北\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow0_col0\" class=\"data row0 col0\">\n",
       "                    29\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow0_col1\" class=\"data row0 col1\">\n",
       "                    0.386667\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow0_col2\" class=\"data row0 col2\">\n",
       "                    0.386667\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow0_col3\" class=\"data row0 col3\">\n",
       "                    38.67%\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow0_col4\" class=\"data row0 col4\">\n",
       "                    38.67%\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_69b3902c_316f_11e8_8cc5_240a64de810d\" class=\"row_heading level4 row1\">\n",
       "                    南\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow1_col0\" class=\"data row1 col0\">\n",
       "                    20\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow1_col1\" class=\"data row1 col1\">\n",
       "                    0.266667\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow1_col2\" class=\"data row1 col2\">\n",
       "                    0.653333\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow1_col3\" class=\"data row1 col3\">\n",
       "                    26.67%\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow1_col4\" class=\"data row1 col4\">\n",
       "                    65.33%\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_69b3902c_316f_11e8_8cc5_240a64de810d\" class=\"row_heading level4 row2\">\n",
       "                    东\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow2_col0\" class=\"data row2 col0\">\n",
       "                    8\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow2_col1\" class=\"data row2 col1\">\n",
       "                    0.106667\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow2_col2\" class=\"data row2 col2\">\n",
       "                    0.76\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow2_col3\" class=\"data row2 col3\">\n",
       "                    10.67%\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow2_col4\" class=\"data row2 col4\">\n",
       "                    76.00%\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_69b3902c_316f_11e8_8cc5_240a64de810d\" class=\"row_heading level4 row3\">\n",
       "                    东南\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow3_col0\" class=\"data row3 col0\">\n",
       "                    5\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow3_col1\" class=\"data row3 col1\">\n",
       "                    0.0666667\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow3_col2\" class=\"data row3 col2\">\n",
       "                    0.826667\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow3_col3\" class=\"data row3 col3\">\n",
       "                    6.67%\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow3_col4\" class=\"data row3 col4\">\n",
       "                    82.67%\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_69b3902c_316f_11e8_8cc5_240a64de810d\" class=\"row_heading level4 row4\">\n",
       "                    北\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow4_col0\" class=\"data row4 col0\">\n",
       "                    4\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow4_col1\" class=\"data row4 col1\">\n",
       "                    0.0533333\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow4_col2\" class=\"data row4 col2\">\n",
       "                    0.88\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow4_col3\" class=\"data row4 col3\">\n",
       "                    5.33%\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow4_col4\" class=\"data row4 col4\">\n",
       "                    88.00%\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_69b3902c_316f_11e8_8cc5_240a64de810d\" class=\"row_heading level4 row5\">\n",
       "                    西南\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow5_col0\" class=\"data row5 col0\">\n",
       "                    4\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow5_col1\" class=\"data row5 col1\">\n",
       "                    0.0533333\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow5_col2\" class=\"data row5 col2\">\n",
       "                    0.933333\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow5_col3\" class=\"data row5 col3\">\n",
       "                    5.33%\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow5_col4\" class=\"data row5 col4\">\n",
       "                    93.33%\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_69b3902c_316f_11e8_8cc5_240a64de810d\" class=\"row_heading level4 row6\">\n",
       "                    西北\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow6_col0\" class=\"data row6 col0\">\n",
       "                    3\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow6_col1\" class=\"data row6 col1\">\n",
       "                    0.04\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow6_col2\" class=\"data row6 col2\">\n",
       "                    0.973333\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow6_col3\" class=\"data row6 col3\">\n",
       "                    4.00%\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow6_col4\" class=\"data row6 col4\">\n",
       "                    97.33%\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_69b3902c_316f_11e8_8cc5_240a64de810d\" class=\"row_heading level4 row7\">\n",
       "                    东北\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow7_col0\" class=\"data row7 col0\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow7_col1\" class=\"data row7 col1\">\n",
       "                    0.0133333\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow7_col2\" class=\"data row7 col2\">\n",
       "                    0.986667\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow7_col3\" class=\"data row7 col3\">\n",
       "                    1.33%\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow7_col4\" class=\"data row7 col4\">\n",
       "                    98.67%\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_69b3902c_316f_11e8_8cc5_240a64de810d\" class=\"row_heading level4 row8\">\n",
       "                    东西\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow8_col0\" class=\"data row8 col0\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow8_col1\" class=\"data row8 col1\">\n",
       "                    0.0133333\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow8_col2\" class=\"data row8 col2\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow8_col3\" class=\"data row8 col3\">\n",
       "                    1.33%\n",
       "                \n",
       "                <td id=\"T_69b3902c_316f_11e8_8cc5_240a64de810drow8_col4\" class=\"data row8 col4\">\n",
       "                    100.00%\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "        </tbody>\n",
       "        </table>\n",
       "        "
      ],
      "text/plain": [
       "<pandas.formats.style.Styler at 0x9828630>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 频率分布情况 - 定性字段\n",
    "# ① 通过计数统计判断不同类别的频率\n",
    "\n",
    "cx_g = data['朝向'].value_counts(sort=True)\n",
    "print(cx_g)\n",
    "# 统计频率\n",
    "\n",
    "r_cx = pd.DataFrame(cx_g)\n",
    "r_cx.rename(columns ={cx_g.name:'频数'}, inplace = True)  # 修改频数字段名\n",
    "r_cx['频率'] = r_cx / r_cx['频数'].sum()  # 计算频率\n",
    "r_cx['累计频率'] = r_cx['频率'].cumsum()  # 计算累计频率\n",
    "r_cx['频率%'] = r_cx['频率'].apply(lambda x: \"%.2f%%\" % (x*100))  # 以百分比显示频率\n",
    "r_cx['累计频率%'] = r_cx['累计频率'].apply(lambda x: \"%.2f%%\" % (x*100))  # 以百分比显示累计频率\n",
    "r_cx.style.bar(subset=['频率','累计频率'], color='#d65f5f',width=100)\n",
    "# 可视化显示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.0096782405524749, 1.0000000009804906, -1.00505686615904, 1.019316156480605)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PnsysWbO67aMn5u61dzZ7M2GRssPdvTVpGwU8B8xy99t62XYM0AFc6O5fMrMW\n4Dfufm6qz2LgVHfvtiRdcmr5pk2bNg3ainVtbW0sWLCA5uZmxo0bNyivsTfbvn07HR0dLF26VKua\ni4iIiIjIkFcoFJg6dSrAVHfvdRq/3pnrAmFRsnem2qYTFjT7YR/bOrAr9ZobgLcnK5CX/DXw/Tpr\nEhEREREREclUXYPrZPGy64DFZjbDzN4CXJ20PW9mLWZ2OoRZaDP7kJm93sxOAr5FOOX728nuvgjs\nD1xrZseY2ULgWODzA3JkIiIiIiIiIntIvTPXAJcSBsi3AGuAFmA+MBI4Cjg46fd40vfHwNeAZ4Fp\n7r4NwN3bgPcAbwHuB04D3u3uTzZ4LMNC+vpryV616zgkO8ojLsojLsojLsojLsojLsojLsqjdvvW\nu4G7vwjMSx5pXcCkVL/rgev72Nd64A311jCcHX/88VmXICnz5lV+G0iWlEdclEdclEdclEdclEdc\nlEdclEftGpm5lgwdeeSRWZcgKTNmzMi6BElRHnFRHnFRHnFRHnFRHnFRHnFRHrXT4FpERERERESk\nnzS4FhEREREREeknDa6HmMqbrku2Km9WL9lSHnFRHnFRHnFRHnFRHnFRHnFRHrXT4HqIefDBB7Mu\nQVK0entclEdclEdclEdclEdclEdclEdclEftzN2zrqEmZjYF2LRp0yamTJkyKK/R1tbGggULaG5u\nZty4cYPyGnuz7du309HRwdKlS5k4cWLW5YiIiIiIiPRLoVBg6tSpAFPdvdBb37pvxSWyJ3V2dlIs\nFrMuY8hqampi/PjxWZchIiIiIrLXa2hwbWaLgPOA8cBdwHnu3lHRZzTwSeBM4HCgFfhXd/9aqs+u\nil07cLS7P9xIXbJ36ezsZMmSJbS3t2ddypB1wAEHsHDhQg2wRUREREQGWd2DazO7BJgHfAT4PXA9\ncAPwnoqupwEnARcBbcAHgJvM7HF3vzfV70zgvtTXT9Rbk+ydisUi7e3tjBkzhqampqzLGXJK71+x\nWNTgWkRERERkkNU1uDYzAy4GFrv7HUnbRcBaMzvM3dMD4++5+y2prx80s1lADkgPrp9290cbK3/4\nWbNmDaeddlrWZexRTU1N0V4DH3seO3bsyLqEPWrOnDl8+ctfzroMSSiPuCiPuCiPuCiPuCiPuCiP\n2tW7WvhxQDPhVPCSewinc5+Y7lh5mnji+QZeU1KOOOKIrEuQFOURlxkzZmRdgqQoj7goj7goj7go\nj7goj7jPbEYmAAAgAElEQVQoj9rVO9AtjSQeKzW4+05gG/Dq3jY0s9cAfwn8T8VTLWb2WzO708ym\n1lnPsHPcccdlXYKkKI+4nHHGGVmXICnKIy7KIy7KIy7KIy7KIy7Ko3b1Dq7HArvcvauivQiM7mkj\nM9uHcG32Wne/O/XUicAU4MPASGCdmU2qsyYRERERERGRTNU7uH4BGGFmlduNJgywe/JF4EBgTrrR\n3e9z91+6ewtwKrCTsMCZiIiIiIiIyJBR7+D6qeTPQ0oNZjYKmABUXZTMzD4HvB042d2f6WnH7r4D\n+DVwcG8FzJw5k1wuV/aYNm0aq1evLuvX0tJCLpfrtv3cuXNZuXJlWVuhUCCXy9HRUX6Z+Lp169iw\nYUNZW2dnJ/l8vtvtoTZu3EhLS0tZW1dXF/l8ntbW1rL2zZs3s2bNmm61rVq1ii1btpS1bd26lXw+\nv/vr0r7Wrl1LoVB+D/O2tjby+Xy3+0Lv6eM4//zzlQd7Xx6VtS1atIhly5aVtbW2tpLL5bq9b8uX\nL2f+/PllbcVikVwu1+29yOfzzJlT9jkcALNnz+7zOEr7GurHUTLUj0N5xHUcyiOu41AecR2H8ojr\nOJRHXMcxnPJYsWJF2Thz8uTJzJo1q9s+emLuXnvncO/qDuBCd1+ZtM0AbgcOdPdnK/p/hjAT/VZ3\nf7yPfY8DHgcWuvsXqjw/Bdi0adMmpkyZUnPN9Whra2PBggU0NzdHuzp1Pp+P9rqH7du309HRwdKl\nS5k4cWK/96c8+meg8xgKcrkct912W9ZlSEJ5xEV5xEV5xEV5xEV5xGW451EoFJg6dSrAVHcv9Na3\nrltxuftOM7sOWGxmTxJW/74auA543sxagC+5+61mdhnwCcL9rfcxsyOT3fze3Z8xs5MI11x/DxgH\nXAY8B9xUT03DTT2fnMjgUx5xufnmm7MuQVKUR1yUR1yUR1yUR1yUR1yUR+3qGlwnLiVcY30L8DLw\nFeASwoJkRwGlKbI5wJ8Ad1RsfwWwGHgWOAtYCHQC64Fz3P25BmoaNkaOHJl1CZKiPOLS1NSUdQmS\nojziojziojziojziojziojxqV/fg2t1fBOYlj7QuYFKq3+F97OdnwOvrfX0RERERERGR2NS7oJmI\niIiIiIiIVNDgeoipXDlasqU84lK50qRkS3nERXnERXnERXnERXnERXnUToPrIWb8+PFZlyApyiMu\nkyZN6ruT7DHKIy7KIy7KIy7KIy7KIy7Ko3YaXA8xJ5xwQtYlSIryiMsFF1yQdQmSojziojziojzi\nojziojziojxqp8G1iIiIiIiISD9pcC0iIiIiIiLSTxpcDzHt7e1ZlyApyiMuW7ZsyboESVEecVEe\ncVEecVEecVEecVEetWtocG1mi8zsKTN7zsy+aWbNVfqMNrMFZrY56fdLMzuros+xZvYDMyua2dbK\n56W77373u1mXICnKIy6XXHJJ1iVIivKIi/KIi/KIi/KIi/KIi/KoXd2DazO7BJgHfAx4B3A0cEOV\nrqcBJwEXAScC3wBuMrO3JPvZD2gBtgLHA9cDN5rZ8XUfxTAyc+bMrEuQFOURl2uuuSbrEiRFecRF\necRFecRFecRFecRFedRu33o6m5kBFwOL3f2OpO0iYK2ZHebuT6S6f8/db0l9/aCZzQJywL3AOYAB\n57n7S8nzpwIfB+5r9ID2drr1U1yUR1x0q4i4KI+4KI+4KI+4KI+4KI+4KI/a1TtzfRzQDNyVarsH\ncMLs9G7u3lFl++dTrzkdWJcMrEvurtyPiIiIiIiISOzqHVwfkfz5WKnB3XcC24BX97ahmb0G+Evg\nf1L7eqyiW2tf+xERERERERGJTb2D67HALnfvqmgvAqN72sjM9iFcU73W3e9O7atYz34ENmzYkHUJ\nkqI84rJs2bKsS5AU5REX5REX5REX5REX5REX5VG7uq65Bl4ARpjZCHfflWofTfeBctoXgQOB91fs\na1RFv772M+x1dVV+riFZUh5xKRb130dMlEdclEdclEdclEdclEdclEft6p25fir585BSg5mNAiYA\nj1bbwMw+B7wdONndn6nY16EV3Q/taT8lM2fOJJfLlT2mTZvG6tWry/q1tLSQy+W6bT937lxWrlxZ\n1lYoFMjlcnR0lF8mvm7dum4zk52dneTz+W73N964cSMtLS1lbV1dXeTzeVpbW8vaN2/ezJo1a7rV\ntmrVqm73kdu6dSv5fH73129729sAWLt2LYVCoaxvW1sb+Xy+2zfAnj6O888/X3mw9+VRWduiRYu6\nfZLZ2tpKLpfr9r4tX76c+fPnl7UVi0VyuVy39yKfzzNnzpxutc2ePbvP47jiiiv2iuMoGerHoTzi\nOg7lEddxKI+4jkN5xHUcyiOu4xhOeaxYsaJsnDl58mRmzZrVbR89MXevvbPZaKADuNDdVyZtM4Db\ngQPd/dmK/p8BzgTe6u6PVzx3GXAucLgnRZjZvcCP3L383Q3PTQE2bdq0iSlTptRccz3a2tpYsGAB\nzc3NjBs3blBeY2+2fft2Ojo6WLp0KRMnTuz3/pRH/wx0HiIiIiIiw02hUGDq1KkAU9290Fvfumau\nk8XLrgMWm9mM5J7VVydtz5tZi5mdDrsHz58g3A97HzM7Mnm8MtndF4H9gWvN7BgzWwgcC3y+nppE\nREREREREslbvaeEAlwLfBm4B1gAtwHxgJHAUUJoimwP8CXAH8HDqcQGAu7cB7wHeAtwPnAa8292f\nbPBYhgVd8xAX5RGXylOSJFvKIy7KIy7KIy7KIy7KIy7Ko3Z1D67d/UV3n+fur3T3A9z9k+7e5e5F\nd5/k7v+Z9Dvc3fep8lic2td6d3+Du49x9ze6+48G8uD2RtWuqZXsKI+4nHvuuVmXICnKIy7KIy7K\nIy7KIy7KIy7Ko3aNzFxLhqZPn551CZKiPOJy+eWXZ12CpCiPuCiPuCiPuCiPuCiPuCiP2mlwPcRo\nYaq4KI+4DNZih9IY5REX5REX5REX5REX5REX5VE7Da5FRERERERE+kmDaxEREREREZF+0uB6iCkU\ner21muxhyiMuK1euzLoESVEecVEecVEecVEecVEecVEetdPgeohpa2vLugRJUR5x0YcdcVEecVEe\ncVEecVEecVEecVEetdPgeog55ZRTsi5BUpRHXK699tqsS5AU5REX5REX5REX5REX5REX5VG7PTa4\nNrNRe+q1RERERERERPakhgbXZrbIzJ4ys+fM7Jtm1lylzygzO9nMlpvZ48CsKn12VTxeNrM/a6Qm\nERERERERkazUPbg2s0uAecDHgHcARwM3VOn6ceBWYCJwaC+7PBN4bfJ4HbC13ppEREREREREslTX\n4NrMDLgYWOzud7j7T4CLgJlmdlhF968BB7j7LMB62e3T7v5o6vFyPTUNN/l8PusSJEV5xCWXy2Vd\ngqQoj7goj7goj7goj7goj7goj9rVO3N9HNAM3JVquwdw4MR0R3dvd/euflUn3Rx//PFZlyApyiMu\n8+bNy7oESVEecVEecVEecVEecVEecVEetat3cH1E8udjpQZ33wlsA17dYA0tZvZbM7vTzKY2uI9h\n48gjj8y6BElRHnGZMWNG1iVIivKIi/KIi/KIi/KIi/KIi/KoXb2D67HArioz0kVgdAOvfyIwBfgw\nMBJYZ2aTGtiPiIiIiIiISGbqHVy/AIwws8rtRhMG2HVx9/vc/Zfu3gKcCuwkLHDWo5kzZ5LL5coe\n06ZNY/Xq1WX9Wlpaql4fMHfuXFauXFnWVigUyOVydHR0lLWvW7eODRs2lLV1dnaSz+dpb28va9+4\ncSMtLS1lbV1dXeTzeVpbW8vaN2/ezJo1a7rVtmrVKrZs2VLWtnXr1qrX9a5du7bbDd3b2trI5/MU\ni+VR7OnjOP/885UHe18elbUtWrSIZcuWlbW1traSy+W6vW/Lly9n/vz5ZW3FYpFcLtftvcjn88yZ\nM6dbbbNnz9Zx6Dh0HDoOHYeOQ8eh49Bx6DgG7ThWrFhRNs6cPHkys2Z1u+lVj8zda+9s9mZgPXC4\nu7cmbaOA54BZ7n5bD9vtAs5296/3sf8fAfe7+4VVnpsCbNq0aRNTpkypueZ6tLW1sWDBApqbmxk3\nbtygvEZ/bdmyhaOOOirrMqravn07HR0dLF26lIkTJ/Z7f8qjfwY6j6Fg9erVvPe97826DEkoj7go\nj7goj7goj7goj7gM9zwKhQJTp04FmOruhd761jtzXSDMLr8z1TadsKDZD+vcVxkzGwdMBrb01Xc4\ne/DBB7MuQVKUR1y0entclEdclEdclEdclEdclEdclEft9q2ns7vvNLPrgMVm9iTwPHA1cB3wvJm1\nAF9y91vNbCxwIH+8DdeBZnYk8Ad332ZmJxGuuf4eMA64jDADftNAHNjeqp7TEmTwKY+43HLLLVmX\nICnKIy7KIy7KIy7KIy7KIy7Ko3b1zlwDXAp8G7gFWAO0APMJC5IdBRyc9Hs/8AjwEGFm+9+Bh4HS\nyffPAmcBPyDcE/t3wF+5+3ONHIiIiIiIiIhIVuqauQZw9xeBeckjrQuYlOp3I3BjL/v5GfD6el9f\nREREREREJDaNzFyLiIiIiIiISIoG10NMtVstSXaUR1yq3ZpBsqM84qI84qI84qI84qI84qI8aqfB\n9RBzxBFHZF2CpCiPuMyYMSPrEiRFecRFecRFecRFecRFecRFedROg+sh5rjjjsu6BElRHnE544wz\nsi5BUpRHXJRHXJRHXJRHXJRHXJRH7TS4FhEREREREeknDa5FRERERERE+mmPDa7NbNSeeq29WWtr\na9YlSIryiMuGDRuyLkFSlEdclEdclEdclEdclEdclEftGhpcm9kiM3vKzJ4zs2+aWXOVPqPM7GQz\nW25mjwOzqvQ51sx+YGZFM9tqZmc1Us9wcu+992ZdgqQoj7hceeWVWZcgKcojLsojLsojLsojLsoj\nLsqjdnUPrs3sEmAe8DHgHcDRwA1Vun4cuBWYCBxaZT/7AS3AVuB44HrgRjM7vt6ahpNZs7p9RiEZ\nUh5xufnmm7MuQVKUR1yUR1yUR1yUR1yUR1yUR+32raezmRlwMbDY3e9I2i4C1prZYe7+RKr714Av\nuHuXme2qsrtzAAPOc/eXgAfN7FTCoPy++g9leBg5cmTWJUiK8ohLU1NT1iVIivKIi/KIi/KIi/KI\ni/KIi/KoXV2Da+A4oBm4K9V2D+DAicDuwbW7t/exr+nAumRgXXI38L46axKRPaSzs5NisZh1GUNW\nU1MT48ePz7oMERERERkE9Q6uj0j+fKzU4O47zWwb8OoG9rW2oq21gf2IyB7Q2dnJkiVLaG/v63Mz\n6ckBBxzAwoULNcAWERER2QvVO7geC+xy966K9iIwuoF9VU6BNbKfYaWlpYUZM2ZkXYYkhlMexWKR\n9vZ2xowZE+3pQT/4wQ846aSTsi6jqtL7VywWh83gev78+Vx11VVZlyEJ5REX5REX5REX5REX5VG7\negfXLwAjzGyEu6evox5N94FyLfuqvD1XI/sZVobLL+VDxXDMo6mpiXHjxmVdRlUTJkyItjaAHTt2\nZF3CHjVp0qSsS5AU5REX5REX5REX5REX5VG7elcLfyr585BSQ3L/6gnAow3sq3IV8UP72s/MmTPJ\n5XJlj2nTprF69eqyfi0tLeRyuW7bz507l5UrV5a1FQoFcrkcHR0dZe3r1q3rdl+3zs5O8vl8t1Nj\nN27cSEtLS1lbV1cX+Xy+272QN2/ezJo1a7rVtmrVKrZs2VLWtnXrVvL5/O6vTzjhBADWrl1LoVAo\n69vW1kY+n+92TeyePo7zzz9feaA8Bvo4lEdQyqOytkWLFrFs2bKyttbWVnK5XLf3bfny5cyfP7+s\nrVgsksvlur0X+XyeOXPmdKtt9uzZfR7HBRdcsFccR8lQPw7lEddxKI+4jkN5xHUcyiOu4xhOeaxY\nsaJsnDl58uS67g5k7l57Z7PRQAdwobuvTNpmALcDB7r7sz1stws4292/nmq7DDgXONyTIszsXuBH\n7j6/yj6mAJs2bdrElClTaq65Hm1tbSxYsIDm5uaoZ79itX37djo6Oli6dCkTJ07s9/6UR/8oj7gM\ndB4iIiIiMvgKhQJTp04FmOruhd761jVz7e47geuAxWY2w8zeAlydtD1vZi1mdjqAmY01syPN7LXJ\n5gcmX09Ivv4isD9wrZkdY2YLgWOBz9dTk4iIiIiIiEjW6j0tHOBS4NvALcAaoAWYD4wEjgIOTvq9\nH3gEeIhwq65/Bx4GlgG4exvwHuAtwP3AacC73f3JBo9lWNBKzXFRHnFRHnGpPO1LsqU84qI84qI8\n4qI84qI8alf34NrdX3T3ee7+Snc/wN0/6e5d7l5090nu/rmk343uPsLd96l4nJva13p3f4O7j3H3\nN7r7jwby4PZG3/3ud7MuQVKUR1yUR1wuueSSrEuQFOURF+URF+URF+URF+VRu0ZmriVDM2fOzLoE\nSVEecVEecbnmmmuyLkFSlEdclEdclEdclEdclEftNLgeYobjrZ9ipjziojziolt3xEV5xEV5xEV5\nxEV5xEV51E6DaxEREREREZF+0uBaREREREREpJ80uB5iKm+kLtlSHnFRHnFZtmxZ1iVIivKIi/KI\ni/KIi/KIi/KonQbXQ0xXV1fWJUiK8oiL8ohLsVjMugRJUR5xUR5xUR5xUR5xUR610+B6iHnb296W\ndQmSojziojzicsUVV2RdgqQoj7goj7goj7goj7goj9rtm3UBIiLSmM7OTn2a3A9NTU1aYV5EREQG\nTEODazNbBJwHjAfuAs5z944q/d4KfBY4BtgKXOTuLannd1Vs4sDR7v5wI3WJiAwXnZ2dLFmyhPb2\n9qxLGbIOOOAAFi5cqAG2iIiIDIi6B9dmdgkwD/gI8HvgeuAG4D0V/V4DrAWWJ30/AXzbzI5y9ydT\nXc8E7kt9/US9NQ0nxWKRpqamrMuQhPKIy3DKo1gs0t7ezpgxY6I95h07djBmzJisy6iq9P4Vi8Vh\nM7hub2/ngAMOyLoMSSiPuCiPuCiPuCiP2tU1uDYzAy4GFrv7HUnbRcBaMzvM3dMD4wuBR9z90qTf\nhUAOOBdIn7j/tLs/2o9jGFbWrFnDGWeckXUZklAecRmOeTQ1NTFu3Lisy6jqO9/5TtR57NixI+sS\n9qhzzz2X2267LesyJKE84qI84qI84qI8alfvgmbHAc2EU8FL7iGczn1iRd/pwJ2lL9z9ZeCHVfpJ\nHaZPn551CZKiPOKiPOKiPOJy+eWXZ12CpCiPuCiPuCiPuCiP2tV7WvgRyZ+PlRrcfaeZbQNeXaXv\nYxVtrYQBelqLmT0LPAB82t031VnTsDJx4sSsS5AU5REX5RGX4ZZH7AvMTZw4kba2tqzL6NFALzCn\nPPpnuC34N2XKlKxLkBTlERflUbt6B9djgV3uXnkz2SIwukrfyp9qlf1OBJ4DDgH+CVhnZse6e2ud\ndYmIiGRGC8z130AuMKc8+k8L/omI1K/e08JfAEaYWeV2o+k+kH4BGNVbP3e/z91/mawgfiqwk7DA\nWY9mzpxJLpcre0ybNo3Vq1eX9WtpaSGXy3Xbfu7cuaxcubKsrVAokMvl6OgoX/B83bp1bNiwoayt\ns7OTfD7f7Qf2xo0baWlpKWvr6uoin8/T2lr+WcHmzZtZs2ZNt9pWrVrFli1bytq2bt1KPp/v1nft\n2rUUCoWytra2NvL5fLdP6vf0cZx//vnKA+Ux0MehPALlMbDHMVB5PPnkk7sXmGtubuahhx5i69at\nNDc3736MHDmS9evXY2Zl7a2trTz44INlbfvttx/r169n586dZe3btm3j/vvvL2trbm7mxz/+Mc88\n80xZ2/bt21m/fn23vg888ABPP/10WdtLL73E+vXraWpqKmvfU8cxZswY2tvb+dCHPqQ89sI8Kr93\nFy1axLJly8raWltbyeVy3f5fWb58OfPnzy9rKxaL5HK5bv9X5PN55syZ06222bNn6zh0HDoOHUdN\nx7FixYqycebkyZOZNWtWt330xNy99s5mbwbWA4eXZpfNbBRh9nmWu9+W6vsw8FV3X5xquwkY6+7v\n62H/PwLud/cLqzw3Bdi0adOmQTs1oa2tjQULFtDc3BztAkGFQiHaUzO2b99OR0cHS5cuHZDTQZVH\n/yiPuCiPuCiPuCiPuAx0HkPBypUr+du//dusy5CE8ojLcM+jUCgwdepUgKnuXuitb70z1wXC7PI7\nU23TCQua/bCi74Z0v2S2ezrwvWo7NrNxwGRgS7XnJYj5+qzhSHnERXnERXnERXnERXnEpfLsGsmW\n8oiL8qhdXddcJ4uXXQcsNrMngeeBq4HrgOfNrAX4krvfCnwe2GhmC4FvAXMBA24EMLOTCNdcfw8Y\nB1xGmAG/aSAObG91yimnZF2CpCiPuCiPuCiPuCiPuAy3PGJfYO6f//mfo/7AY7gtMHfttddmXYKk\nKI/a1bugGcClhGunbwFeBr4CXAKMBI4CDgZw9wfM7AzgM8AC4D5ghrs/n+znWeAsYCHQSTjd/Bx3\nf67hoxERERGRqGiBuf7TAnMiQ0Pdg2t3fxGYlzzSuoBJFX2/RZi1rrafnwGvr/f1RURERGToKBaL\nuxeYa2pqyrqcIaf0/hWLRQ2uRSLXyMy1iIiIiEhdmpqaol1gLnY7duzIugQRqUG9C5pJxqrd5kay\nozziojziojziojziojziojziUu32SZId5VE7Da6HmOOPPz7rEiRFecRFecRFecRFecRFecRFecRl\n3rzKq08lS8qjdhpcDzFHHnlk1iVIivKIi/KIi/KIi/KIi/KIi/KIy4wZM7IuQVKUR+00uBYRERER\nERHpJw2uRURERERERPpJg+shZsuWLVmXICnKIy7KIy7KIy7KIy7KIy7KIy6rV6/OugRJUR61a+hW\nXGa2CDgPGA/cBZzn7h1V+r0V+CxwDLAVuMjdW1LPHwtcC7wJaAMuc/evNVLTcHHvvfdy1FFHZV2G\nJJRHXJRHXJRHXJRHXJRHXIZbHp2dnRSLxazL6NGSJUs44YQTsi6jR01NTQN6z3Hl0T8DnUd/1D24\nNrNLgHnAR4DfA9cDNwDvqej3GmAtsDzp+wng22Z2lLs/aWb7AS3AncBc4DTgRjN7xN3va/B49npN\nTU1ZlyApyiMuyiMuyiMuyiMuyiMuwymPzs5OlixZQnt7e9al9Oh3v/sdCxYsyLqMHh1wwAEsXLhw\nQAZ0yqP/BjKP/qprcG1mBlwMLHb3O5K2i4C1ZnaYuz+R6n4h8Ii7X5r0uxDIAecCVwDnAEaY9X4J\neNDMTgU+DmhwLSIiIiIywIrFIu3t7YwZMybaDxVGjRpFc3Nz1mVUVXr/isXigAzmlEf/DHQe/VXv\nzPVxQDPhVPCSewAHTgTSg+vphFlpANz9ZTP7YdKv9Py6ZGBdcjfwvjprEhERERGROjQ1NTFu3Lis\ny6hq3333jbY2gB07dgz4PpVH4wYjj0bVu6DZEcmfj5Ua3H0nsA14dZW+j1W0tab69fW8iIiIiIiI\nyJBQ78z1WGCXu3dVtBeB0VX6Vl6Zn+7X1/OVRgP86le/qqfeumzbto2Ojg6ee+45Ro/uqYxs/eY3\nv+HXv/511mVUtXPnTl544QV+/vOf09bW1u/9KY/+UR5xUR5xUR5xUR5xUR5xUR5xUR5xGeg8qkmN\nP/sMyNy95h2b2QeAm4GR7r4r1f4UcJW7fy7V9jwwz92/nGr7V+AUd/8LM/sFsMrdF6We/1iyn/2r\nvPaZgFYSFxERERERkT3tLHf/em8d6p25fir58xDCKdyY2ShgAvBolb6HVrQdmurX1/OV7gLOAh4H\ndtZZt4iIiIiIiEi9RgOvoXzdsarqHVwXCAPbdwIrk7bphAXNfljRd0PSbzGAmY1I+n4m9fy5Zmb+\nx+nzvwa+X+2Fk/to9/pJgYiIiIiIiMgA+1Etnepa0CxZvOw6YLGZzTCztwBXJ23Pm1mLmZ2edP88\ncLyZLTSzY4BrCLfeujF5/ovA/sC1ZnaMmS0Ejk22ExERERERERky6l0tHOBS4NvALcAaoAWYD4wE\njgIOBnD3B4AzgA8BPwX+HJjh7s8nz7cB7wHeAtwPnAa8292f7MfxiIiIiIiIiOxxdS1oJiIiIiIi\nIiLdNTJzLSIiIiIiIiIpGlxHyMyuMrPP1tj3LjN7f+rrHWY2afCqk7R6spLBYcFaM5uSdS3DQX/f\nbzP7hpldONB1SXVmtsLMLkp9/Ssze2uWNUnv9D0SB/1+NfAa/fmhLAaHfn8aHBpcD32jgH1SX+s8\nfxluTgXeBPwi60KGCb3fQ8tI6r8ziNTJzL5kZl1m9mLy6DKzXRV/70p9/aKZLc267r3VAOWh368G\nXqM/P5TF4KgpDzP7XgPfT3+3Zw4hPhpcZyiZUShWPoC/B+ZVe87MLu1rtxWvccygHcAwMkhZSYPM\n7MvJf+YvExZWbAZ2mNnLyWNX6fnUn+dlXPaQVcf7nX7fd/bwPfNe4KoevmfOyvRAhyH9jBgY7v5R\ndx/p7qPcfRQwEXg5+Xok8Fvg9aWvkz5T9D0yOBrJw90X9LFb/X7VgEH6ea0sGtRIHsAtFd9P04Ct\nyd/HEj7s2K/i++m/MjvIjOnT7Ay5+3lAt/9AzOwqYB93v6j7VrUxs8OBLwDTzOw17v5s45XKYGYl\nDTkPmAtMBu4Djgae7mObnYNd1F6soffb3XdVNprZN4D17q7bLmbIzCYQbqX5ATM7zt0fzrqmvcwo\n4OXU10bFhIa7n1xtQ32PDIo+86iVfr+qWyM/P16oZcfKoiED8ftT+vup9EGHJmwTeiMyZuHe4NvT\nD+AC4BOV7WZ2h5nl058oAdWunbsI2Az8gfDJrP6zGQB1ZrU263r3Zu7e5e5F4EzgTnf/dfL1IcAq\ndy9WeXQb6EltGn2/zeyhKt8zpwHLqnzPfCHLY9xbmNnSip8RH67S7SxgC3AQMFUD64FhZvdbckok\n8MpO7lIAAAbDSURBVAQw0pLTKIFXAf9rqdMmk230PTJI6syjS79fDY5Gfn4AX1UWg6MfP8/bU98/\nPwBel/z9D8mun0l9Pw3rnykaXGdvDHCmu48DFgBvdffR7j4amA18PHnubGC0u59BOOOgdB3d+ir7\nPA54m7vPdvfWPXIUw0M9WY3JsM5hwcxGEgYO6VOPxgEnZlPR3q3B97sJeHPyffE5YHJyutgY4ELg\nfclz/wyMHpzKh5fk1Nb0z4ivVOk2FTjD3d/h7g/uyfr2cvsAM5JTJd8KPJ46jfJ3wBuSvx/EH68f\n1ffI4KknjxH6/Wrw1PvzQ1kMrgZ/no8g+f8p2fae5O/jkufHJ1+fyDA/M1qD6ziUTqkYC1yTav8U\n8JrKzu6+q/Sg+qIO57j7Twe8SoE6s5JB9V6gC1hnZq8ws1cArwAofZ16jMy00r1Do+936XtmAvAf\nSf8RwELgwD1X/vBRw8+Ii9y9ZU/XNUyU/r0fCzzSw3M9baPvkYFXVx76/WrQ1P3zQ1kMqkZ+nhuN\n/f827AzrTxYi9J/Am8xsLPAGwmDt3xvYj/6BD76Bykoa93rCIjXbqzxXrPh6I/DmQa9o79bf93sR\n8J9mZsAZwDZ3/+qAVykShzOAb9a5jb5HBk8jeVSj368aMxg/r5VF4xrOI/n/6XTgH1J9lEWKZq4j\nYWZfBx4iLIn/FHBH8tTDZnZjP/b7ygEoT1IGKyupj7v///buJkSOIgzj+P9JQERRQwKyElzF6MWb\nH0QhEBI/Ei+C5igEo7KCF0GRgEoigh5UPBiNekoOm73EgOJJFJMQlAjqQUQUJCgG9SBBN36sZGNe\nDzWjvUNnpqd7emZ2+vnBHrqnuremXmrfqu3u6l0RsTL7A6wH5jv3R4Qn1hWVbO8AkHQM+JSUoP8A\n9gBXSPpB0vOj+D6WSFo16jpMGknbgOuB7MT4b+CadpGcY9xHalImHgXP6/FVQXXna8eiPxXj8Rhp\nwbn3WtuLpMXNKvWnSeLJ9ZiIiPsiYjoipkkrH+5tb0fE/Z3lJa2QdDewrsep90p6oo46N1W/sbL6\nSFor6UtJV+d8NiPpmeHXanKVaG8BRMTGTJ95F3gy02eerr3iDSVpE+n56m52SnqtRxkr7lZgHzAT\nEfOZ/bPAwdYCQD93HuQ+Upu+4+HxVT3K5GvHoj4lx0/3ArtJt+T/A+n2fWAO+LzVn47XV+vlwbeF\nj4d1kk5mti8FQtL21naQFjpB0lXADuAB4Cz/L4pyPlPAfI8yVlzhWFm9JF0IvA0cj4jvc4ocBT6Q\ntCYiHh1m3SZRhfbepKWr568Gtkja1doO4NUaqtxIki4HtgMPAauAP3scMkXOZM9K+4K0CNnh7M6I\n2E0alCJpNfBL5mP3kfoUjofHV/XpN384FvWqkM+PAXdFxGfZwhGxgxQvJN0IHBp8rZcPX7keDyci\n4sr2D+lq6OuZfdPACdJVoH3ALaSVqa8FOpe7P016lgJJ1wE3A18N64s0QNFYWY0krQQOAr8Cj+SV\niYhvgQ3AVkkHWsdYCSXaezbT3kc7+kz7qly2z5wZwtdoihdIVxeeBaaBjzo+/520yi6SpoDNOEcM\n0kLnRC5H59jLfaQ+RePh8VVN+swfWyQdAPbjWNSiYj4/FRGf9PgVjZ9b+sr1GJN0AbCGlFQ3kBYZ\nuCciFrsc9jJwqLW6XwDv4Fs0apcTq4XR1mjivUFaWfd24JykG4DfgDtIzysCEBE/StoMHCG9Pu25\nEdR1EpRp76e6nVDSxaRV98+R1i/wVYfBeDibI9LaM0u8ArwpaU9r+2NSnrDBeF/pvbzdFHom0X1k\nIIrGI4CtEXG2SzmPr8rpJ3/cRsofsxHRLV87FuWVHT8BfJOTUzoJONmr0CTz5Hr08l4v0LaWdBU0\nSLf2PZgzsV5yfES8KOkl0nuWz/RIFNafvmI1lBo1137g64j4C0DSYdIt+guk98H+JyJ+krSRlDys\nnDLtPQ/M5Jyr3Y/WAx+2tk8B2+qperMUyBFzwJyki4DFHv+stf4EcGdEHOlWSNIa0nuW28fknQfc\nR6rqKx454yWPrwZjEPnasRicsvn8cdJ7rr/rdnJJNwFv1VHx5UIR3eYLVjdJl5BuWzrvHwZJCgdq\n5Byr8ea2H64i7S3pMuB0t3KSVrQWRDFrHPcRayLn6/HieAyWJ9dmZmZmZmZmFTX+oXMzMzMzMzOz\nqjy5NjMzMzMzM6vIk2szMzMzMzOzijy5NjMzMzMzM6vIk2szMzMzMzOzijy5NjMzMzMzM6vIk2sz\nMzMzMzOzijy5NjMzMzMzM6vIk2szMzMzMzOziv4F4Ioq/QBVgJcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb1314e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ugcQWBNlEN1oToa2vYZlEGQz8HmhdCt62wH99DsiYllAE1kJOCkvIiVUY/doA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\ndx+aHtAStT2YXlScOnug/2pRt66sLHjpJbj6arj0Uu2IcTA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tRGNNkAF698aTkMwwhrlt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\nQbZYLBaLJQr4fwjZTYF5bSkeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb2cd080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 频率分布情况 - 定量字段\n",
    "# ② 绘制频率直方图、饼图\n",
    "\n",
    "plt.figure(num = 1,figsize = (12,2))\n",
    "r_cx['频率'].plot(kind = 'bar',\n",
    "                 width = 0.8,\n",
    "                 rot = 0,\n",
    "                 color = 'k',\n",
    "                 grid = True,\n",
    "                 alpha = 0.5)\n",
    "plt.title('参考总价分布频率直方图')\n",
    "# 绘制直方图\n",
    "\n",
    "plt.figure(num = 2)\n",
    "plt.pie(r_cx['频数'],\n",
    "       labels = r_cx.index,\n",
    "       autopct='%.2f%%',\n",
    "       shadow = True)\n",
    "plt.axis('equal')\n",
    "# 绘制饼图"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
